MAT1830 · Discrete mathematics for computer science

Overview

Computation relies on discrete structures and formal reasoning to describe, analyse and solve problems involving data, systems, algorithms and computational processes. This unit introduces foundational discrete mathematics for computer science, including set theory, functions and relations, propositional and predicate logic, introductory methods of proof, sequences and series, elementary number theory, counting and combinatorics, and discrete probability. You will learn how these mathematical ideas are used to model computational objects and relationships, reason about program behaviour, analyse structured and recursive processes, count possibilities, and work with uncertainty. The unit emphasises practical mathematical reasoning and interpretation in computer science contexts, helping you build confidence with the formal language and problem-solving techniques used in later computing studies. You will develop the ability to manipulate discrete structures, apply logical and proof techniques, reason with sequences and number patterns, apply counting and probability methods, and explain mathematical ideas clearly. The unit provides essential foundations for later study in algorithms, programming, artificial intelligence, cybersecurity, data science, software systems and theoretical computer science.

Areas of study: Applied mathematics Computational science Mathematics Software engineering

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Apply discrete structures, including sets, functions and relations, to model and solve foundational problems in mathematics and computer science;
2. Use logic, introductory proof methods, sequences and elementary number theory to express, analyse and justify mathematical and computational claims;
3. Apply counting, combinatorial reasoning and elementary discrete probability to analyse possibilities and uncertainty in computational contexts;
4. Communicate discrete mathematical reasoning using appropriate notation, terminology, structured working and interpretation;
5. Plan, monitor and reflect on your development of discrete mathematical fluency through focused practice, feedback and responsible use of learning supports.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. Applied sessions start from Week 2 of the semester.

Student experience, not official rules. Nothing here changes what the Handbook says.